A multi-sensor network for direction finding of moving ferromagnetic objects inside water by magnetic anomaly

Nazlibilek, Sedat; Ege, Yavuz; Kalender, Osman

doi:10.1016/j.measurement.2009.06.002

Cited by 16 publications

(6 citation statements)

References 17 publications

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“…In literature, some studies have been developed such as separating metal mine cases confirming to standards from magnetic samples that have got nonstandard geometry by finding buried magnetic materials' geometrical features parallel to the soil surface in order to decrease the false alarms number [9][10].Therefore, with the aim of decreasing the number of false alarms, it is tried to identify and classify buried magnetic materials' geometrical features parallel to the soil surface by using magnetic anomaly method in this study.…”

Section: Figure 1 Application Of Electromagnetic Induction Methodsmentioning

confidence: 99%

Determination of Buried Magnetic Materials Geometric Dimensions

Ege¹,

Kakilli²,

Çıtak³

et al. 2016

SIPIJ

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It is important to find buried magnetic material's geometric features that are parallel to the soil surface in order to determine anti-tank and anti-personnel mine compatible to standards. So that it is possible to decrease the number of false alarms by separating the samples that have got non-standard geometries. For this purpose, in this study the anomalies occurred at horizontal component of the earth's magnetic field by buried samples are determined with magnetic sensor. In the study, KMZ51 AMR is used as the magnetic sensor. The position-controlled movement of the sensor along x-y axis is provided with 2D scanning system. Trigger values of sensor output are evaluated with respect to the scanning field. The experiments are redone for the samples at different geometries and variables are defined for geometric analysis. The experimental conclusions obtained from this paper will be discussed in detail.

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Section: Figure 1 Application Of Electromagnetic Induction Methodsmentioning

confidence: 99%

Determination of Buried Magnetic Materials Geometric Dimensions

Ege¹,

Kakilli²,

Çıtak³

et al. 2016

SIPIJ

Self Cite

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“…A magnetic anomaly detector (MAD) is an instrument often used by the military to detect underwater vehicles. 17,18 This is because a ferromagnetic material creates a magnetic field disturbance. 19 Similarly, this technique could be used to detect the small magnetic anomalies created by ferromagnetic ROVs and UUVs.…”

Section: Electromagnetic Signaturesmentioning

confidence: 99%

The non-acoustic signatures of underwater vehicles

Slimming,

Beniwal,

Devrelis

et al. 2023

Ocean Sensing and Monitoring XV

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Submersible vehicles, such as Uncrewed Underwater Vehicles (UUVs), can be lost underwater and it is desirable to locate them before they go missing or collide with other vehicles. These objects generate inherent signatures when they move in the ocean. With a better understanding of these signatures, more information regarding the motion of underwater vehicles can be inferred using LiDAR technology. In this paper, the authors review existing literature on various non-acoustic signatures of a submerged body, namely, the electromagnetic, biological and thermal signatures, turbulent wake patterns, internal waves and vortex structures. The authors discuss how these signatures evolve both spatially and temporally. Furthermore, the review investigates how environmental and operational parameters such as the stratification of the medium, vehicle speed, shape and depth affect the non-acoustic signatures. Underwater vehicles operating at low speeds and large depths have bioluminescent, Kelvin wake and Bernoulli hump signatures that are difficult to detect on the surface. Thermal signatures, vortex wakes and far wakes are only likely to be detected at the vehicle’s depth. This is primarily due to the ocean stratification which suppresses the vertical motion of underwater turbulent signatures. Thermal signatures appear to be most likely to be detected. The study concludes that relying on a single signature to detect submersibles is not advisable and future methods for underwater vehicle detection should use multiple sensors to detect complementary signatures.

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“…(i) The BE is exposed to a high level, constant magnetic field, and measurements are made with magnetic field measurement sensors over it. MA is detected, and the BE is interpreted by means of the data obtained [29][30][31][32]. Since an external fixed magnetic field is applied to the BE, the measurement made in this way is named an active measurement.…”

Section: Introductionmentioning

confidence: 99%

Detection and Imaging of Underground Objects for Distinguishing Explosives by Using a Fluxgate Sensor Array

2019

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Active and passive techniques are two different techniques with which to detect buried explosives. In practice, the most preferred active method works by broadcasting a signal underground. This signal may stimulate the buried explosive and cause it to explode. It is important to eliminate or minimize this drawback to ensure the safety of the detector operator. In this respect, it is important to increase the studies on the passive detection technique which is not currently used in practice. The aim of this study was to passively detect improvised explosive devices without stimulating them, and to classify underground objects as explosive or non-explosive. A fluxgate sensor array having 33 components was used for passive magnetic field measurements, and the nearest neighborhood algorithm was preferred for classifying the resulting data. In experimental studies, 33 different samples having different amounts of ferromagnetic properties were used. Successful imaging and classification were achieved for the measurements up to 20 cm below the surface of soil. Data were recorded as 32 × 25 matrices, and then they were reduced to 32 × 2 matrices having the same features. Samples having explosive properties were distinguished from other underground objects with success rates of 86% and 95% for 32 × 25 and 32 × 2 data matrices, respectively. Classification times for 32 × 25 and 32 × 2 data matrices were 42 ms and 3.62 ms, respectively. For data groups where the best results were obtained for the data matrices, frame numbers classified in one second were calculated as 23.80 and 276.2, respectively. False alarm rate achieved was 5.31%. The experimental results proved the successes of the matrices reduction and classification approach. One of the most common problems encountered in passive detecting techniques is that the sensor position affects the measurements negatively. In this paper, a solution has been proposed for this important problem.

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A multi-sensor network for direction finding of moving ferromagnetic objects inside water by magnetic anomaly

Cited by 16 publications

References 17 publications

Determination of Buried Magnetic Materials Geometric Dimensions

Determination of Buried Magnetic Materials Geometric Dimensions

The non-acoustic signatures of underwater vehicles

Detection and Imaging of Underground Objects for Distinguishing Explosives by Using a Fluxgate Sensor Array

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